13.4 Knowledge Representation via Attractor Neural Networks 257 N hg = Ss" WikPk k=1,hHi,j 1 Awa = — (pips — hisps — hyapi) 13.4.2 Knowledge Representation via Cell Assemblies Hopfield nets and their ilk play a dual role: as computational algorithms, and as conceptual models of brain function. In CogPrime they are used as inspiration for slightly different, artificial economics based computational algorithms; but their hypothesized relevance to brain function is nevertheless of interest in a CogPrime context, as it gives some hints about the potential connection between low-level neural net mechanics and higher-level cognitive dynamics. Hopfield nets lead naturally to a hypothesis about neural knowledge representation, which holds that a distinct mental concept is represented in the brain as either: 1. a set of “cell assemblies”, where each assembly is a network of neurons that are interlinked in such a way as to fire in a (perhaps nonlinearly) synchronized manner 2. adistinct temporal activation pattern, which may occur in any one (or more) of a particular set of cell assemblies For instance, this hypothesis is perfectly coherent if one interprets a “mental concept” as a SMEPH (defined in Chapter 14) ConceptNode, i.e. a fuzzy set of perceptual stimuli to which the organism systematically reacts in different ways. Also, although we will focus mainly on declarative knowledge here, we note that the same basic representational ideas can be applied to procedural and episodic knowledge: these may be hypothesized to correspond to temporal activation patterns as characterized above. In the biology literature, perhaps the best-articulated modern theories championing the cell assembly view are those of Gunther Palm [Pal&82, HAGO7] and Susan Greenfield [SF05, CSGO7]. Palm focuses on the dynamics of the formation and interaction assemblies of cortical columns. Greenfield argues that each concept has a core cell assembly, and that when the concept rises to the focus of att